分类技术在学习预测中的意义 残疾

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摘要翻译：
本研究旨在说明两种分类技术的重要性。决策树和聚类在学龄儿童学习障碍预测中的应用。LDs影响了大约10%的入学儿童。一段时间以来，有特殊学习障碍的儿童的问题一直是家长和教师关注的问题。决策树和聚类是数据挖掘中用于分类和预测的强大而流行的工具。从决策树中提取的不同规则用于学习障碍的预测。聚类是将一组观察结果分配到称为聚类的子集中，这些子集有助于发现LD受影响儿童的不同体征和症状（属性）。本文采用J48算法构造决策树，用K-means算法生成聚类。通过应用这些分类技术，可以识别任何儿童的LD。
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英文标题：
《Significance of Classification Techniques in Prediction of Learning
  Disabilities》
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作者：
Julie M. David And Kannan Balakrishnan
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.
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PDF链接：
https://arxiv.org/pdf/1011.0628

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